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Artificial Intelligence-Clinical Decision Support System (AI-CDSS) has the potential to assist physicians in heart failure diagnosis. The aim of this work was to evaluate the diagnostic accuracy of an AI-CDSS for heart failure. AI-CDSS for cardiology was developed with a hybrid (expert-driven and machine-learning-driven) approach of knowledge acquisition to evolve the knowledge base with heart failure diagnosis. A retrospective cohort of 1198 patients with and without heart failure was used for the development of AI-CDSS (training dataset, <jats:italic>n<\/jats:italic>\u2009=\u2009600) and to test the performance (test dataset, <jats:italic>n<\/jats:italic>\u2009=\u2009598). A prospective clinical pilot study of 97 patients with dyspnea was used to assess the diagnostic accuracy of AI-CDSS compared with that of non-heart failure specialists. The concordance rate between AI-CDSS and heart failure specialists was evaluated. In retrospective cohort, the concordance rate was 98.3% in the test dataset. The concordance rate for patients with heart failure with reduced ejection fraction, heart failure with mid-range ejection fraction, heart failure with preserved ejection fraction, and no heart failure was 100%, 100%, 99.6%, and 91.7%, respectively. In a prospective pilot study of 97 patients presenting with dyspnea to the outpatient clinic, 44% had heart failure. The concordance rate between AI-CDSS and heart failure specialists was 98%, whereas that between non-heart failure specialists and heart failure specialists was 76%. In conclusion, AI-CDSS showed a high diagnostic accuracy for heart failure. Therefore, AI-CDSS may be useful for the diagnosis of heart failure, especially when heart failure specialists are not available.<\/jats:p>","DOI":"10.1038\/s41746-020-0261-3","type":"journal-article","created":{"date-parts":[[2020,4,8]],"date-time":"2020-04-08T10:03:12Z","timestamp":1586340192000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":130,"title":["Artificial intelligence for the diagnosis of heart failure"],"prefix":"10.1038","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0146-2189","authenticated-orcid":false,"given":"Dong-Ju","family":"Choi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9611-1490","authenticated-orcid":false,"given":"Jin Joo","family":"Park","sequence":"additional","affiliation":[]},{"given":"Taqdir","family":"Ali","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5962-1587","authenticated-orcid":false,"given":"Sungyoung","family":"Lee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,8]]},"reference":[{"key":"261_CR1","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1001\/jama.289.2.194","volume":"289","author":"MM Redfield","year":"2003","unstructured":"Redfield, M. 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